8 results about "Random effects model" patented technology

The invention is suitable for the technical field of intelligent medical treatment, and provides an intelligent data analysis assistant based on mesh meta analysis, which comprises a processor, a data acquisition module, a preprocessing module, an analysis modeling module, a statistical analysis module, a visualization module, a generation module, an AI auxiliary analysis module and a storage module are arranged in the processor; the analysis modeling module is used for automatically constructing a treatment network diagram on the basis of multi-element mesh Meta analysis and in combination with a Bayesian multi-level analysis model and a mixed linear model, displaying direct and indirect comparison relationships among different treatments and providing selection of a fixed effect model and a random effect model; the assistant helps a user to quickly understand complex data by automatically constructing a treatment network diagram, displaying direct and indirect comparison relationships among different treatments, providing selection of a fixed effect model and a random effect model, automatically calculating direct comparison and indirect comparison results of treatment effects, providing consistency check, improving retrieval analysis ability and helping the user to quickly understand the complex data.

The invention relates to an immunotherapy prognosis evaluation system based on combined penalty likelihood modeling, and belongs to the technical field of precision medicine. Based on a generalized linear hybrid model, various types of clinical endpoints, such as objective remission rate and disease-progression-free lifetime, are effectively integrated. By introducing the random effect, the model can capture potential correlation between end points, and the statistical effectiveness of analysis is enhanced. For the colinearity between clonality mutation features, key variables are screened through a penalty likelihood method, the influence of multiple colinearity is reduced, and the explanatory property and prediction accuracy of the model are improved. In order to overcome the challenge of limited sample size, cross-subgroup data fusion likelihood modeling is introduced, the strategy not only retains the model specificity of each subgroup, but also improves the adaptability of the model to small sample data and the prediction accuracy through information sharing among the subgroups.

The application provides a distortion correction method and device, electronic equipment and storage medium, wherein the method comprises: determining actual coordinates corresponding to a plurality of calibration points according to target images obtained by photographing the plurality of calibration points by a target camera; determining principal point coordinates and focal length of the target camera according to the actual coordinates corresponding to the plurality of calibration points; constructing a mixed effect model of the target camera based on the actual coordinates corresponding to the plurality of calibration points, the principal point coordinates and the focal length of the target camera, wherein the mixed effect model comprises a fixed effect model and a random effect model; and performing distortion correction on an imaging area of the target camera through the mixed effect model. The mixed effect model constructed by the application can capture global trends of distortion and correct local residual distortion, thereby achieving high-precision correction of full-field distortion, improving adaptability and robustness of distortion characteristics of different cameras, and being able to adapt to significant differences in regional distortion of large-field cameras.

The application discloses a psychological test data processing method and system based on tangent variational approximation, relates to the technical field of data processing and statistical calculation, and comprises the following steps: obtaining psychological test data, wherein the psychological test data comprises test information, item information and reaction results of the test information to the item information; the test information and the item information are in a cross corresponding relationship; inputting the psychological test data into a pre-established cross random effect model and estimating based on tangent variational approximation to output psychological test data estimation results, wherein the psychological test data estimation results comprise posterior mean and variance of random effects of all testers, posterior mean and variance of random effects of all items, posterior mean and a covariance matrix of fixed effect parameters, and corrected random effect variance component estimation values; and the application improves the test accuracy and efficiency during psychological test.

The invention relates to the field of integrated circuit production, in particular to an optical proximity correction method, device and equipment and a storage medium. Inputting the mask pattern to be processed into a pre-trained optical proximity correction model capable of predicting a random effect, and outputting a corresponding average photoresist image; reducing and improving the brightness of the average photoresist image according to preset random effect model parameters to obtain a first changed photoresist image and a second changed photoresist image; obtaining an inner ring contour edge in the first changed photoresist image according to a preset model threshold value, and obtaining an outer ring contour edge in the second changed photoresist image according to the model threshold value; and taking an area between the inner ring contour edge and the outer ring contour edge as a probability contour band after photoetching. According to the method, the hot spot prediction of the large-scale integrated circuit chip layout is realized on the premise of not obviously increasing the operation time and the calculation power occupation.

The invention discloses a regulation and control method for cigarette flavor. The regulation and control method comprises the following steps: constructing a flavor standardized database comprising material characteristics, experimental conditions and sensory evaluation results; according to the flavor standardization database, based on a tobacco text mining strategy, identifying a core flavor-causing substance; carrying out heterogeneity test and effect quantity combination on the core flavor-causing substances through Meta analysis, and determining quantitative correlation between each substance and a sensory effect by adopting a random effect model so as to determine an optimal regulation and control range; based on single-factor ANOVA and clustering analysis, carrying out production area raw material type selection; the sensory quality is accurately regulated and controlled. According to the regulation and control method of the cigarette flavor, a complete regulation and control system is constructed by integrating multidisciplinary technologies, directional, quantitative and efficient regulation and control of the flavor are achieved, a scientific basis is provided for accurate regulation and control of sweet fragrance, cool feeling and fruit sweet flavor of cigarettes, and a technical support is also provided for diversified innovation of flavor in the tobacco industry.

The present invention discloses a Bayesian parameter estimation method for a free-space continuous-variable quantum communication system. First, a priori information of the free-space channel is constructed using a Bayesian random-effect model. Second, channel state information is obtained through pilot information. Due to the sparsity of the free-space channel, compressed sensing technology is used to achieve efficient design and accurate reconstruction of the pilot signal. Finally, system parameter estimation is achieved in the free-space channel using the priori information and channel state information. Therefore, the Bayesian estimation method proposed in the present invention takes into account the overall characteristics of the channel and is applicable to free-space channels with fluctuating characteristics, providing a post-processing solution for realizing a free-space continuous-variable quantum secure communication system.

The invention discloses a reliability modeling and evaluation method of a multi-batch aluminum silver oxide battery rupture membrane, which aims at the actual demand of reliability evaluation of the aluminum silver oxide battery rupture membrane, comprehensively considers the bimodal distribution characteristic of the opening pressure of the rupture membrane in the batch and the difference between different batches, and improves the reliability of the rupture membrane. According to the method, technical means of Gaussian mixture distribution, a random effect model, an Expectation-Maximization (EM) algorithm and the like are applied, and the reliability modeling and evaluation problem of intra-batch and inter-batch differences of multiple batches of rupture membranes at the same time is solved. The method belongs to the technical field of reliability modeling and evaluation.